The trigger design for AdvCam

The AdvCam is a next-generation camera for the Large-Sized Telescopes of the Cherenkov Telescope Array Observatory, based on silicon photomultipliers. Its fully digital readout system enables the design of new, sophisticated trigger logic. The Large-Sized Telescopes aim to cover the low-energy range of the cosmic gamma-ray spectrum, with a threshold starting at about 20 GeV, using the existing photomultiplier tube camera. The AdvCam, along with the new trigger logic, as shown by simulations, lowers the detectable energy threshold to 13 GeV. The proposed trigger logic has a multilevel structure. The first level involves fast coincidences among small pixel regions at a rate of approximately 1 GHz, while the second level processes all camera pixels within an approximately 10-nanosecond time window. Different families of machine learning algorithms optimized for FPGAs form the second-level trigger. In this work, we consider two main approaches: Deep Neural Networks and Density-Based Spatial Clustering of Applications with Noise, both running with latencies below 1 microsecond at a 1 MHz rate. This work provides a detailed description of the trigger chain and its performance, as studied through simulation.

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Publication Details

Published
2025-09-23
DOI
https://doi.org/10.22323/1.501.0590
Citations
1
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
1.49

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The trigger design for AdvCam

Leonid Burmistrov
1 citations
Advanced Malware Detection Techniques
1.49
article

The trigger design for AdvCam

Leonid Burmistrov
article en
1 citations

Abstract

The AdvCam is a next-generation camera for the Large-Sized Telescopes of the Cherenkov Telescope Array Observatory, based on silicon photomultipliers. Its fully digital readout system enables the design of new, sophisticated trigger logic. The Large-Sized Telescopes aim to cover the low-energy range of the cosmic gamma-ray spectrum, with a threshold starting at about 20 GeV, using the existing photomultiplier tube camera. The AdvCam, along with the new trigger logic, as shown by simulations, lowers the detectable energy threshold to 13 GeV. The proposed trigger logic has a multilevel structure. The first level involves fast coincidences among small pixel regions at a rate of approximately 1 GHz, while the second level processes all camera pixels within an approximately 10-nanosecond time window. Different families of machine learning algorithms optimized for FPGAs form the second-level trigger. In this work, we consider two main approaches: Deep Neural Networks and Density-Based Spatial Clustering of Applications with Noise, both running with latencies below 1 microsecond at a 1 MHz rate. This work provides a detailed description of the trigger chain and its performance, as studied through simulation.

National Science Foundation, Sveučilište u Zagrebu, Comunidad de Madrid, Ministerio de Ciencia, Innovación y Universidades, Centres de Recerca de Catalunya, European Commission, Deutsche Forschungsgemeinschaft, Ministry of Education, Culture, Sports, Science and Technology, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Fundação de Amparo à Pesquisa do Estado de São Paulo, Ministerstvo Školství, Mládeže a Tělovýchovy, Bundesministerium für Bildung und Forschung, Generalitat de Catalunya, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Max-Planck-Gesellschaft, Narodowe Centrum Nauki, Hrvatska Zaklada za Znanost, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Fundação Araucária, Centre National de la Recherche Scientifique, Ministerio de Ciencia e Innovación, Istituto Nazionale di Astrofisica, Sveučilište Josipa Jurja Strossmayera u Osijeku, Junta de Andalucía, Ministério da Ciência, Tecnologia, Inovações e Comunicações, Istituto Nazionale di Fisica Nucleare, Japan Society for the Promotion of Science, Consejo Superior de Investigaciones Científicas, Staatssekretariat für Bildung, Forschung und Innovation, European Regional Development Fund, Agencia Estatal de Investigación, Institut National de Physique Nucléaire et de Physique des Particules, Institute for Cosmic Ray Research, University of Tokyo
Openalex Percentile: Top 14%
Advanced Malware Detection Techniques
1.49
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